library(dplyr)
library(knitr)
library(maptools)
library(rgdal)
library(TreeSegmentation)
library(sp)
library(ggplot2)
library(rgl)
library(lidR)
library(pander)

knit_hooks$set(webgl = hook_webgl)
opts_chunk$set(warning=F,message=F)

#set color ramp for treeID
col = pastel.colors(200)

#set data paths
path_to_tiles="../data/2017/Lidar/"

#set cores
cores<-6
#cores<-15

1 Load in ground-truth

shps<-list.files("../data/ITCs/test/",pattern=".shp",full.names = T)
itcs<-lapply(shps,readOGR,verbose=F)

names(itcs)<-sapply(itcs,function(x){
  id<-unique(x$Plot_ID)
  return(id)
  })

2 Example Pipeline

2.1 Read in Data

ground_truth<-itcs[[3]]
fname<-get_tile_filname(ground_truth)
inpath<-paste("../data/2017/Lidar/",fname,sep="")
tile<-readLAS(inpath)
tile@crs<-CRS("+init=epsg:32617")
plot(tile)

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2.2 Confirm overlap

plot(extent(tile),col='red')
plot(extent(ground_truth),col='blue',add=T)

2.3 Compute Segmentation

silva<-silva2016(path=inpath,output="all")
## [1] "Computing Ground Model"
## [1] "Computing Canopy Model"
## [1] "Clustering Trees"
##    user  system elapsed 
##   3.722   0.044   3.792 
## [1] "Creating tree polygons"
dalponte<-dalponte2016(path=inpath,output="all")
## [1] "Computing Ground Model"
## [1] "Computing Canopy Model"
## [1] "Clustering Trees"
##    user  system elapsed 
##   3.940   0.039   4.004 
## [1] "Creating tree polygons"
li<-li2012(path=inpath,output="all")
## [1] "Computing Ground Model"
## [1] "Computing Canopy Model"
## [1] "Clustering Trees"
##    user  system elapsed 
##   0.093   0.001   0.094 
## [1] "Creating tree polygons"
watershed_result<-watershed(path=inpath,output="all")
## [1] "Computing Ground Model"
## [1] "Computing Canopy Model"
## [1] "Clustering Trees"
##    user  system elapsed 
##   4.911   0.054   5.008 
## [1] "Creating tree polygons"

2.4 View 3d segmentation

plot(silva$tile,color="treeID",col=col)

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2.4.1 Overlay ground truth and predictions

plot(silva$convex)

plot(ground_truth,col='red')
plot(silva$convex,add=T)

#plot(dalponte$convex,add=T)

2.4.2 CHM versus predicted polygons

chm=canopy_model(silva$tile)
plot(chm,ext=extent(ground_truth))
plot(ground_truth,add=T,col='red')
plot(silva$convex,add=T)

Okay that’s not great, but let’s keep going for the moment.

2.4.3 Compare Methods

Silva v Dalponte

plot(chm,ext=extent(ground_truth))
plot(silva$convex,add=T)
plot(dalponte$convex,add=T,col=rgb(0,0,255,20,maxColorValue=255))

Li versus watershed

plot(chm,ext=extent(ground_truth))
plot(li$convex,add=T)
plot(watershed_result$convex,add=T,col=rgb(0,0,255,20,maxColorValue=255))

How many tree predictions?

ptlist<-list(silva=silva$tile,dalponte=dalponte$tile,li=li$tile,watershed=watershed_result$tile)
unique_total<-sapply(ptlist,function(x) length(unique(x@data$treeID)))
df<-data.frame(Algorthm=c(names(ptlist)),Total_Trees=as.numeric(unique_total))
pandoc.table(df,style="rmarkdown")
## 
## 
## | Algorthm  | Total_Trees |
## |:---------:|:-----------:|
## |   silva   |     157     |
## | dalponte  |     157     |
## |    li     |     392     |
## | watershed |     82      |

2.5 Assign Trees

Each tree is assigned based on the maximum overlap. Pairwise membership is done using a Hungarian Algorithm. See clue::solve_LSAP.

assignment<-assign_trees(ground_truth,prediction=silva$convex)

2.6 Calculate Intersection over union

#loop through assignments and get jaccard statistic for each assignment pair
statdf<-calc_jaccard(assignment=assignment,ground_truth = ground_truth,prediction=silva$convex)
ggplot(statdf) + geom_histogram(aes(IoU)) + labs(x="Intersection over union") + theme_bw()

mean(statdf$IoU)
## [1] 0.209751
median(statdf$IoU)
## [1] 0.119387

3 As a wrapper for one tile, multiple methods

results<-evaluate(ground_truth=itcs[[2]],algorithm = c("silva","dalponte"),path_to_tiles=path_to_tiles,plot_results=T)
## [1] "Silva"
## [1] "Computing Ground Model"
## [1] "Computing Canopy Model"
## [1] "Clustering Trees"
##    user  system elapsed 
##  18.342   0.290  18.693 
## [1] "Creating tree polygons"
## [1] "Dalponte"
## [1] "Computing Ground Model"
## [1] "Computing Canopy Model"
## [1] "Clustering Trees"
##    user  system elapsed 
##  20.556   0.284  20.903 
## [1] "Creating tree polygons"
ggplot(results,aes(y=IoU,x=Method)) + geom_boxplot() + theme_bw()

results %>% group_by(Method) %>% summarize(mean=mean(IoU),median=median(IoU))
## # A tibble: 2 x 3
##   Method    mean median
##   <chr>    <dbl>  <dbl>
## 1 dalponte 0.281  0.304
## 2 silva    0.265  0.259

4 Parallel wrapper across all tiles

system.time(results_all<-evaluate_all(itcs=itcs,algorithm = c("dalponte","li","watershed","silva"),path_to_tiles=path_to_tiles,cores=cores,extra=F,plot_results = T))
## [1] "ITC  has no overlap with cropped tile"
##     user   system  elapsed 
##    0.215    0.024 1050.444
#plot results
ggplot(results_all,aes(y=IoU,x=Method)) + geom_boxplot() + theme_bw()

#Compute test statistics
test_statistic<-results_all %>% group_by(Method) %>% summarize(mean=mean(IoU),median=median(IoU)) %>% mutate(Date=format(Sys.time(), "%m/%d/%y %H:%M:%S"))
test_statistic
## # A tibble: 4 x 4
##   Method     mean median Date             
##   <chr>     <dbl>  <dbl> <chr>            
## 1 dalponte  0.271  0.260 05/09/18 15:10:53
## 2 li        0.271  0.249 05/09/18 15:10:53
## 3 silva     0.266  0.252 05/09/18 15:10:53
## 4 watershed 0.220  0.208 05/09/18 15:10:53
write.table(test_statistic,"Results/results.csv",append = T,col.names = F,sep=",",row.names = F)